Checking Data

d<-read.csv("C://Users/ASUS/Desktop/Descriptive Statistics/Research/country.csv")
d$region<-factor(d$region)
levels(d$region)<-c("Eastern Africa","Middle Africa","Northern Africa","Southern Africa","Western Africa","Caribbean","Central America","South America","North America","Eastern Asia","Southeast Asia","Southern Asia","Western Asia","Eastern Europe","Northern Europe","Southern Europe","Western Europe","Oceania","USSR")
d$develop<-factor(d$develop)
levels(d$develop)<-c("Developing country","Developed country")

Principal Component Analysis

library(FactoMineR)
## Warning: package 'FactoMineR' was built under R version 3.6.3
res.pca <- PCA(d[,2:10], graph = FALSE)
## Warning in PCA(d[, 2:10], graph = FALSE): Missing values are imputed by the mean
## of the variable: you should use the imputePCA function of the missMDA package
print(res.pca)
## **Results for the Principal Component Analysis (PCA)**
## The analysis was performed on 122 individuals, described by 9 variables
## *The results are available in the following objects:
## 
##    name               description                          
## 1  "$eig"             "eigenvalues"                        
## 2  "$var"             "results for the variables"          
## 3  "$var$coord"       "coord. for the variables"           
## 4  "$var$cor"         "correlations variables - dimensions"
## 5  "$var$cos2"        "cos2 for the variables"             
## 6  "$var$contrib"     "contributions of the variables"     
## 7  "$ind"             "results for the individuals"        
## 8  "$ind$coord"       "coord. for the individuals"         
## 9  "$ind$cos2"        "cos2 for the individuals"           
## 10 "$ind$contrib"     "contributions of the individuals"   
## 11 "$call"            "summary statistics"                 
## 12 "$call$centre"     "mean of the variables"              
## 13 "$call$ecart.type" "standard error of the variables"    
## 14 "$call$row.w"      "weights for the individuals"        
## 15 "$call$col.w"      "weights for the variables"

Variances of the principal components

eigenvalues <- res.pca$eig
print(eigenvalues)
##        eigenvalue percentage of variance cumulative percentage of variance
## comp 1 6.14036147             68.2262385                          68.22624
## comp 2 1.05979242             11.7754714                          80.00171
## comp 3 0.83770655              9.3078505                          89.30956
## comp 4 0.45823761              5.0915290                          94.40109
## comp 5 0.30782358              3.4202621                          97.82135
## comp 6 0.09071211              1.0079124                          98.82926
## comp 7 0.05284439              0.5871599                          99.41642
## comp 8 0.04320697              0.4800775                          99.89650
## comp 9 0.00931489              0.1034988                         100.00000

Scree Plot

barplot(eigenvalues[, 2], names.arg=1:nrow(eigenvalues), 
        main = "Variances",
        xlab = "Principal Components",
        ylab = "Percentage of variances",
        col ="steelblue")
# Add connected line segments to the plot
lines(x = 1:nrow(eigenvalues), eigenvalues[, 2], 
      type="b", pch=19, col = "red")

#Make the scree plot using the package factoextra 
library(factoextra)
## Warning: package 'factoextra' was built under R version 3.6.1
## Loading required package: ggplot2
## Warning: package 'ggplot2' was built under R version 3.6.1
## Welcome! Related Books: `Practical Guide To Cluster Analysis in R` at https://goo.gl/13EFCZ
fviz_screeplot(res.pca, ncp=10)

From the above Scree plot we find that, we have to select the column no 1 to 10.

Prepearing Data

data2<-d[1:122,1:10]
print(data2)
##                  country    pop92 urban   gdp lifeexpm lifeexpf birthrat
## 1                Burundi    6.022   8.0   200       51       55     46.0
## 2               Ethiopia   51.070  11.0   130       50       53     45.0
## 3                  Kenya   26.164  26.0   385       60       64     44.0
## 4             Madagascar   12.596  22.0   200       51       55     47.0
## 5                 Malawi    9.605  15.0   200       48       51     52.0
## 6              Mauritius    1.082  41.0  2300       66       74     19.0
## 7             Mozambique   15.469  19.0   120       46       49     46.0
## 8                 Rwanda    8.206   5.0   310       51       55     52.0
## 9                Somalia    7.235  36.0   170       56       55     46.0
## 10              Tanzania   27.791  32.0   260       50       55     50.0
## 11                Uganda   19.386  11.0   300       50       52     51.0
## 12                Zambia    8.745  49.0   380       55       59     48.0
## 13              Zimbabwe   11.033  25.0   660       60       64     41.0
## 14                Angola    8.902  29.0   950       43       47     46.0
## 15              Cameroon   12.658  40.0  1010       55       60     44.0
## 16  Central African Rep.    3.029  47.0   440       46       49     44.0
## 17                  Chad    5.238  32.0   190       39       41     42.0
## 18                 Congo    2.376  41.0  1070       52       56     43.0
## 19                 Gabon    1.106  45.0  4400       51       56     28.0
## 20                 Zaire   39.084  44.0   180       52       56     45.0
## 21               Algeria   26.666  51.0  2130       66       68     31.0
## 22                 Egypt   56.386  44.0   720       58       62     33.0
## 23                 Libya   44.850  70.0  6800       66       71     36.0
## 24               Morocco   26.708  50.0  1060       63       67     30.0
## 25                 Sudan   28.305  22.0   450       53       54     44.0
## 26               Tunisia    8.445  49.0  1320       70       74     26.0
## 27              Botswana    1.300  25.0  2384       59       65     36.0
## 28               Lesotho    1.848  16.0   240       60       63     35.0
## 29               Namibia    1.574  33.0  1400       58       63     45.0
## 30          South Africa   41.688  60.0  2600       62       67     35.0
## 31                 Benin    4.997  20.0   400       49       52     49.0
## 32          Burkina Faso    9.653   8.0   205       52       53     50.0
## 33                 Ghana   16.185  33.0   400       53       56     46.0
## 34                Guinea    7.783  26.0   410       41       45     46.0
## 35               Liberia    2.462  46.0   440       54       59     45.0
## 36                  Mali    8.641  25.0   265       43       47     51.0
## 37            Mauritania    2.059  34.0   520       44       50     49.0
## 38                 Niger    8.052  21.0   300       42       45     58.0
## 39               Nigeria   88.500  35.0   230       48       50     46.0
## 40               Senegal    8.205  30.0   615       54       57     44.0
## 41          Sierra Leone    4.456  33.0   330       43       48     46.0
## 42                  Togo    3.958  25.0   400       54       58     48.0
## 43                  Cuba   10.846  72.0  1580       74       79     18.0
## 44    Dominican Republic    7.515  60.0   950       66       70     27.0
## 45                 Haiti    6.431  29.0   440       53       55     43.0
## 46               Jamaica    2.506  52.0  1400       72       76     24.0
## 47     Trinidad & Tobago    1.285  64.0  3600       68       73     21.0
## 48            Costa Rica    3.187  50.0  1810       75       79     27.0
## 49           El Salvador    5.574  45.0  1010       68       75     33.0
## 50             Guatemala    9.784  39.0  1260       61       66     34.0
## 51              Honduras    4.949  40.0   960       64       68     38.0
## 52                Mexico   92.380  72.0  3200       69       76     29.0
## 53             Nicaragua    3.878  60.0   425       61       66     37.0
## 54                Panama    2.529  53.0  1150       73       77     26.0
## 55             Argentina   32.901  87.0  3100       67       74     20.0
## 56               Bolivia    7.323  51.0   690       59       64     34.0
## 57                Brazil  158.000  76.0  2540       62       68     26.0
## 58                 Chile   13.528  84.0  2200       71       77     21.0
## 59              Colombia   34.296  65.4  1300       69       74     24.0
## 60               Equador   10.933  54.0  1070       67       72     28.0
## 61                Guyana    0.739  35.0   300       61       68     21.0
## 62              Paraguay    4.929  46.0  1460       71       74     33.0
## 63                  Peru   22.767  70.0   920       63       67     28.0
## 64               Uruguay    3.121  86.0  2935       69       76     17.0
## 65             Venezuela   20.675  83.0  2590       71       78     28.0
## 66                Canada   27.351  77.0 19400       74       81     14.0
## 67         United States  256.561  76.0 22470       72       79     14.0
## 68                 China 1169.619  27.0   360       69       72     22.0
## 69                 Japan  124.460  77.0 19100       77       82     10.0
## 70           North Korea   22.227  60.0  1035       66       72     24.0
## 71           South Korea   44.149  74.0  6300       67       73     15.0
## 72              Mongolia    2.305  58.0   900       63       68     34.0
## 73              Cambodia    7.249  12.0   130       48       51     37.0
## 74             Indonesia  195.000  31.0   630       59       64     26.0
## 75                  Laos    4.440  19.0   200       49       52     44.0
## 76              Malaysia   18.410  38.0  2670       66       71     30.0
## 77               Myanmar   42.642  25.0   530       57       61     29.0
## 78             Singapore    2.792 100.0 13900       73       78     18.0
## 79              Thailand   57.624  20.0  1630       67       71     20.0
## 80               Vietnam   68.964  20.0   230       63       67     29.0
## 81           Afghanistan   16.095  18.0   220       45       43     44.0
## 82            Bangladesh  119.000  24.0   200       55       54     36.0
## 83                Bhutan    1.660  95.0   199       50       48     40.0
## 84                 India  886.362  28.0   380       57       58     30.0
## 85                  Iran   61.183  57.0  1500       64       66     44.0
## 86                 Nepal   20.086   8.0   165       51       51     38.0
## 87              Pakistan  121.644  32.0   380       56       57     43.0
## 88             Sri Lanka   17.631  26.0   410       69       74     21.0
## 89                  Iraq   18.445  70.0  1950       62       64     45.0
## 90                Israel    4.748  89.0 12500       76       79     21.0
## 91                Jordan    3.557  68.0  1012       70       73     46.7
## 92                Kuwait    1.318  95.0  6200       72       76     32.0
## 93               Lebanon    3.439  84.0  1400       66       71     28.0
## 94                  Oman    1.587  10.0  4852       65       69       NA
## 95          Saudi Arabia   16.900  78.0  5800       65       68     38.0
## 96                 Syria   13.730  50.0  2300       65       67     44.0
## 97                Turkey   59.640  61.0  3400       68       72     28.0
## 98   United Arab Emirate    2.522  81.0 12100       70       74     20.0
## 99                 Yemen   10.394  29.0   545       49       51     51.0
## 100             Bulgaria    8.868  67.0  5300       69       76     13.0
## 101              Hungary   10.333  62.0  5700       66       75     12.0
## 102               Poland   38.385  62.0  4300       68       76     14.0
## 103              Romania   23.169  55.0  3100       68       74     14.0
## 104              Denmark    5.163  86.0 17700       72       78     12.0
## 105              Finland    5.004  61.0 16200       72       80     12.0
## 106              Ireland    3.531  57.0 11200       72       78     15.0
## 107               Norway    4.294  75.0 17100       74       81     14.0
## 108               Sweden    8.602  85.0 17200       75       81     13.0
## 109       United Kingdom   57.798  90.0 15900       73       79     14.0
## 110              Albania    3.285  35.0  1300       72       79     23.0
## 111               Greece   10.064  63.0  7730       75       80     11.0
## 112                Italy   57.904  67.0 16700       74       81     11.0
## 113             Portugal   10.448  34.0  8400       71       78     12.0
## 114                Spain   39.118  79.0 12400       75       82     11.0
## 115              Austria    7.867  54.0 20895       74       81     12.0
## 116              Belgium   10.016  96.0 17300       73       80     10.0
## 117               France   57.287  74.0 18300       74       82     13.0
## 118          Netherlands   15.112  88.3 16600       75       81     13.0
## 119          Switzerland    6.828  60.0 21700       76       83     12.0
## 120            Australia   17.567  85.0 18054       74       80     15.0
## 121          New Zealand    3.347  76.0 14000       72       80     15.0
## 122     Papua New Guinea    4.006  15.0   800       55       56     34.0
##     deathrat infmr fertrate
## 1         14 106.0      6.1
## 2         15 113.0      6.3
## 3          8  68.0      7.6
## 4         15  93.0      6.5
## 5         18 136.0      6.9
## 6          6  22.0      1.9
## 7         17 134.0      6.2
## 8         15 108.0      8.0
## 9         13 116.0      6.5
## 10        16 103.0      7.0
## 11        15  91.0      6.8
## 12        11  77.0      7.0
## 13         8  59.0      5.3
## 14        19 151.0      6.3
## 15        11  81.0      5.7
## 16        19 135.0      5.8
## 17        22 136.0      5.8
## 18        13 110.0      5.8
## 19        15 100.0      5.3
## 20        14  97.0      6.0
## 21         7  57.0      5.2
## 22        10  80.0      4.2
## 23         6  60.0      6.7
## 24         8  56.0      4.2
## 25        14  85.0      6.3
## 26         5  38.0      3.4
## 27         9  43.0      5.9
## 28        10  74.0      5.6
## 29        10  66.0      5.7
## 30         8  51.0      4.2
## 31        16 115.0      6.9
## 32        16 117.0      6.4
## 33        13  86.0      6.2
## 34        21 144.0      6.1
## 35        13 119.0      6.4
## 36        21 110.0      6.6
## 37        19  89.0      6.4
## 38        23 125.0      7.0
## 39        17 110.0      6.8
## 40        13  80.0      6.2
## 41        21 148.0      6.4
## 42        12  94.0      6.0
## 43         7  12.0      1.7
## 44         7  56.0      3.3
## 45        15 104.0      4.4
## 46         6  17.0      2.4
## 47         6  18.0      2.5
## 48         4  12.0      3.0
## 49         5  26.0      4.5
## 50         8  56.0      5.4
## 51         7  56.0      4.9
## 52         5  29.0      3.1
## 53         8  57.0      5.0
## 54         5  17.0      2.9
## 55         9  31.0      2.8
## 56        19  83.0      5.8
## 57         7  67.0      3.2
## 58         6  18.0      2.7
## 59         5  37.0      3.3
## 60         6  60.0      4.3
## 61         7  51.0      2.4
## 62         5  28.0      4.3
## 63         8  59.0      4.0
## 64        10  22.0      2.5
## 65         4  23.0      3.5
## 66         7   7.3      1.7
## 67         9  10.0      1.9
## 68         7  33.0      2.2
## 69         7   4.0      1.8
## 70         6  30.0      3.2
## 71         6  23.0      1.8
## 72         6  47.0      5.2
## 73        15 121.0      4.4
## 74         8  70.0      2.9
## 75        16 107.0      5.3
## 76         6  27.0      3.1
## 77        10  68.0      3.7
## 78         5   6.0      1.7
## 79         7  35.0      2.2
## 80         8  48.0      3.7
## 81        20 164.0      6.8
## 82        13 112.0      5.1
## 83        17 126.0      5.5
## 84        11  81.0      4.1
## 85        10  66.0      5.4
## 86        14  90.0      5.5
## 87        14 105.0      5.9
## 88         6  21.0      2.5
## 89         8  84.0      5.9
## 90         6   9.0      2.7
## 91         5  38.0      6.9
## 92         2  15.0      4.3
## 93         7  43.0      3.1
## 94        NA  40.0      7.1
## 95         7  69.0      7.1
## 96         7  45.0      6.3
## 97         6  54.0      3.2
## 98         3  23.0      4.3
## 99        16 118.0      5.7
## 100       12  13.0      1.8
## 101       14  14.0      1.8
## 102        9  14.0      2.1
## 103       10  22.0      2.0
## 104       11   7.0      1.5
## 105       10   6.0      1.7
## 106        9   8.0      2.4
## 107       11   7.1      1.7
## 108       11   6.0      1.7
## 109       11   8.0      1.8
## 110        5  27.0      2.7
## 111        9  10.0      1.7
## 112       10   8.0      1.5
## 113       10  10.0      1.7
## 114        8   6.0      1.7
## 115       11   8.0      1.5
## 116       10   8.0       NA
## 117        9   7.0      1.8
## 118        9   7.0      1.5
## 119       10   5.0      1.6
## 120        8   8.0      1.8
## 121        8  10.0      1.8
## 122       11  55.0       NA

Descriptive statistics

summary(data2)
##         country        pop92              urban             gdp       
##  Afghanistan:  1   Min.   :   0.739   Min.   :  5.00   Min.   :  120  
##  Albania    :  1   1st Qu.:   4.444   1st Qu.: 28.25   1st Qu.:  400  
##  Algeria    :  1   Median :   9.900   Median : 48.00   Median : 1110  
##  Angola     :  1   Mean   :  40.749   Mean   : 48.78   Mean   : 4158  
##  Argentina  :  1   3rd Qu.:  27.190   3rd Qu.: 69.50   3rd Qu.: 4375  
##  Australia  :  1   Max.   :1169.619   Max.   :100.00   Max.   :22470  
##  (Other)    :116                                                      
##     lifeexpm        lifeexpf        birthrat        deathrat    
##  Min.   :39.00   Min.   :41.00   Min.   :10.00   Min.   : 2.00  
##  1st Qu.:53.25   1st Qu.:56.00   1st Qu.:20.00   1st Qu.: 7.00  
##  Median :64.00   Median :68.00   Median :31.00   Median :10.00  
##  Mean   :61.90   Mean   :66.31   Mean   :31.29   Mean   :10.46  
##  3rd Qu.:70.75   3rd Qu.:76.00   3rd Qu.:44.00   3rd Qu.:14.00  
##  Max.   :77.00   Max.   :83.00   Max.   :58.00   Max.   :23.00  
##                                  NA's   :1       NA's   :1      
##      infmr           fertrate    
##  Min.   :  4.00   Min.   :1.500  
##  1st Qu.: 18.00   1st Qu.:2.400  
##  Median : 55.50   Median :4.300  
##  Mean   : 58.49   Mean   :4.279  
##  3rd Qu.: 92.50   3rd Qu.:6.025  
##  Max.   :164.00   Max.   :8.000  
##                   NA's   :2

Correlation matrix Histogram

library(PerformanceAnalytics)
## Warning: package 'PerformanceAnalytics' was built under R version 3.6.3
## Loading required package: xts
## Warning: package 'xts' was built under R version 3.6.1
## Loading required package: zoo
## Warning: package 'zoo' was built under R version 3.6.1
## 
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric
## Registered S3 method overwritten by 'xts':
##   method     from
##   as.zoo.xts zoo
## 
## Attaching package: 'PerformanceAnalytics'
## The following object is masked from 'package:graphics':
## 
##     legend
chart.Correlation(data2[,2:10], histogram=TRUE, pch=19)

Cosstabulation about Country And Country Type

f<-table(d$country,d$develop)
f
##                       
##                        Developing country Developed country
##   Afghanistan                           0                 1
##   Albania                               1                 0
##   Algeria                               0                 1
##   Angola                                0                 1
##   Argentina                             1                 0
##   Australia                             0                 1
##   Austria                               0                 1
##   Bangladesh                            1                 0
##   Belgium                               0                 1
##   Benin                                 0                 1
##   Bhutan                                0                 1
##   Bolivia                               0                 1
##   Botswana                              0                 1
##   Brazil                                0                 1
##   Bulgaria                              1                 0
##   Burkina Faso                          0                 1
##   Burundi                               0                 1
##   Cambodia                              0                 1
##   Cameroon                              0                 1
##   Canada                                0                 1
##   Central African Rep.                  0                 1
##   Chad                                  0                 1
##   Chile                                 0                 1
##   China                                 0                 1
##   Colombia                              0                 1
##   Congo                                 0                 1
##   Costa Rica                            0                 1
##   Cuba                                  0                 1
##   Denmark                               0                 1
##   Dominican Republic                    0                 1
##   Egypt                                 0                 1
##   El Salvador                           0                 1
##   Equador                               0                 1
##   Ethiopia                              0                 1
##   Finland                               0                 1
##   France                                0                 1
##   Gabon                                 0                 1
##   Ghana                                 0                 1
##   Greece                                0                 1
##   Guatemala                             0                 1
##   Guinea                                0                 1
##   Guyana                                0                 1
##   Haiti                                 0                 1
##   Honduras                              0                 1
##   Hungary                               0                 1
##   India                                 1                 0
##   Indonesia                             0                 1
##   Iran                                  0                 1
##   Iraq                                  0                 1
##   Ireland                               0                 1
##   Israel                                1                 0
##   Italy                                 0                 1
##   Jamaica                               0                 1
##   Japan                                 0                 1
##   Jordan                                0                 1
##   Kenya                                 0                 1
##   Kuwait                                0                 1
##   Laos                                  0                 1
##   Lebanon                               0                 1
##   Lesotho                               0                 1
##   Liberia                               0                 1
##   Libya                                 0                 1
##   Madagascar                            0                 1
##   Malawi                                0                 1
##   Malaysia                              0                 1
##   Mali                                  0                 1
##   Mauritania                            0                 1
##   Mauritius                             0                 1
##   Mexico                                0                 1
##   Mongolia                              1                 0
##   Morocco                               0                 1
##   Mozambique                            0                 1
##   Myanmar                               0                 1
##   Namibia                               1                 0
##   Nepal                                 0                 1
##   Netherlands                           0                 1
##   New Zealand                           0                 1
##   Nicaragua                             0                 1
##   Niger                                 0                 1
##   Nigeria                               0                 1
##   North Korea                           0                 1
##   Norway                                0                 1
##   Oman                                  1                 0
##   Pakistan                              1                 0
##   Panama                                0                 1
##   Papua New Guinea                      0                 1
##   Paraguay                              0                 1
##   Peru                                  0                 1
##   Poland                                0                 1
##   Portugal                              0                 1
##   Romania                               1                 0
##   Rwanda                                0                 1
##   Saudi Arabia                          0                 1
##   Senegal                               0                 1
##   Sierra Leone                          0                 1
##   Singapore                             0                 1
##   Somalia                               0                 1
##   South Africa                          1                 0
##   South Korea                           0                 1
##   Spain                                 1                 0
##   Sri Lanka                             0                 1
##   Sudan                                 0                 1
##   Sweden                                1                 0
##   Switzerland                           0                 1
##   Syria                                 0                 1
##   Tanzania                              0                 1
##   Thailand                              0                 1
##   Togo                                  0                 1
##   Trinidad & Tobago                     0                 1
##   Tunisia                               0                 1
##   Turkey                                0                 1
##   Uganda                                0                 1
##   United Arab Emirate                   0                 1
##   United Kingdom                        0                 1
##   United States                         0                 1
##   Uruguay                               0                 1
##   Venezuela                             0                 1
##   Vietnam                               0                 1
##   Yemen                                 0                 1
##   Zaire                                 0                 1
##   Zambia                                0                 1
##   Zimbabwe                              0                 1
f<-data.frame(f)
f
##                     Var1               Var2 Freq
## 1            Afghanistan Developing country    0
## 2                Albania Developing country    1
## 3                Algeria Developing country    0
## 4                 Angola Developing country    0
## 5              Argentina Developing country    1
## 6              Australia Developing country    0
## 7                Austria Developing country    0
## 8             Bangladesh Developing country    1
## 9                Belgium Developing country    0
## 10                 Benin Developing country    0
## 11                Bhutan Developing country    0
## 12               Bolivia Developing country    0
## 13              Botswana Developing country    0
## 14                Brazil Developing country    0
## 15              Bulgaria Developing country    1
## 16          Burkina Faso Developing country    0
## 17               Burundi Developing country    0
## 18              Cambodia Developing country    0
## 19              Cameroon Developing country    0
## 20                Canada Developing country    0
## 21  Central African Rep. Developing country    0
## 22                  Chad Developing country    0
## 23                 Chile Developing country    0
## 24                 China Developing country    0
## 25              Colombia Developing country    0
## 26                 Congo Developing country    0
## 27            Costa Rica Developing country    0
## 28                  Cuba Developing country    0
## 29               Denmark Developing country    0
## 30    Dominican Republic Developing country    0
## 31                 Egypt Developing country    0
## 32           El Salvador Developing country    0
## 33               Equador Developing country    0
## 34              Ethiopia Developing country    0
## 35               Finland Developing country    0
## 36                France Developing country    0
## 37                 Gabon Developing country    0
## 38                 Ghana Developing country    0
## 39                Greece Developing country    0
## 40             Guatemala Developing country    0
## 41                Guinea Developing country    0
## 42                Guyana Developing country    0
## 43                 Haiti Developing country    0
## 44              Honduras Developing country    0
## 45               Hungary Developing country    0
## 46                 India Developing country    1
## 47             Indonesia Developing country    0
## 48                  Iran Developing country    0
## 49                  Iraq Developing country    0
## 50               Ireland Developing country    0
## 51                Israel Developing country    1
## 52                 Italy Developing country    0
## 53               Jamaica Developing country    0
## 54                 Japan Developing country    0
## 55                Jordan Developing country    0
## 56                 Kenya Developing country    0
## 57                Kuwait Developing country    0
## 58                  Laos Developing country    0
## 59               Lebanon Developing country    0
## 60               Lesotho Developing country    0
## 61               Liberia Developing country    0
## 62                 Libya Developing country    0
## 63            Madagascar Developing country    0
## 64                Malawi Developing country    0
## 65              Malaysia Developing country    0
## 66                  Mali Developing country    0
## 67            Mauritania Developing country    0
## 68             Mauritius Developing country    0
## 69                Mexico Developing country    0
## 70              Mongolia Developing country    1
## 71               Morocco Developing country    0
## 72            Mozambique Developing country    0
## 73               Myanmar Developing country    0
## 74               Namibia Developing country    1
## 75                 Nepal Developing country    0
## 76           Netherlands Developing country    0
## 77           New Zealand Developing country    0
## 78             Nicaragua Developing country    0
## 79                 Niger Developing country    0
## 80               Nigeria Developing country    0
## 81           North Korea Developing country    0
## 82                Norway Developing country    0
## 83                  Oman Developing country    1
## 84              Pakistan Developing country    1
## 85                Panama Developing country    0
## 86      Papua New Guinea Developing country    0
## 87              Paraguay Developing country    0
## 88                  Peru Developing country    0
## 89                Poland Developing country    0
## 90              Portugal Developing country    0
## 91               Romania Developing country    1
## 92                Rwanda Developing country    0
## 93          Saudi Arabia Developing country    0
## 94               Senegal Developing country    0
## 95          Sierra Leone Developing country    0
## 96             Singapore Developing country    0
## 97               Somalia Developing country    0
## 98          South Africa Developing country    1
## 99           South Korea Developing country    0
## 100                Spain Developing country    1
## 101            Sri Lanka Developing country    0
## 102                Sudan Developing country    0
## 103               Sweden Developing country    1
## 104          Switzerland Developing country    0
## 105                Syria Developing country    0
## 106             Tanzania Developing country    0
## 107             Thailand Developing country    0
## 108                 Togo Developing country    0
## 109    Trinidad & Tobago Developing country    0
## 110              Tunisia Developing country    0
## 111               Turkey Developing country    0
## 112               Uganda Developing country    0
## 113  United Arab Emirate Developing country    0
## 114       United Kingdom Developing country    0
## 115        United States Developing country    0
## 116              Uruguay Developing country    0
## 117            Venezuela Developing country    0
## 118              Vietnam Developing country    0
## 119                Yemen Developing country    0
## 120                Zaire Developing country    0
## 121               Zambia Developing country    0
## 122             Zimbabwe Developing country    0
## 123          Afghanistan  Developed country    1
## 124              Albania  Developed country    0
## 125              Algeria  Developed country    1
## 126               Angola  Developed country    1
## 127            Argentina  Developed country    0
## 128            Australia  Developed country    1
## 129              Austria  Developed country    1
## 130           Bangladesh  Developed country    0
## 131              Belgium  Developed country    1
## 132                Benin  Developed country    1
## 133               Bhutan  Developed country    1
## 134              Bolivia  Developed country    1
## 135             Botswana  Developed country    1
## 136               Brazil  Developed country    1
## 137             Bulgaria  Developed country    0
## 138         Burkina Faso  Developed country    1
## 139              Burundi  Developed country    1
## 140             Cambodia  Developed country    1
## 141             Cameroon  Developed country    1
## 142               Canada  Developed country    1
## 143 Central African Rep.  Developed country    1
## 144                 Chad  Developed country    1
## 145                Chile  Developed country    1
## 146                China  Developed country    1
## 147             Colombia  Developed country    1
## 148                Congo  Developed country    1
## 149           Costa Rica  Developed country    1
## 150                 Cuba  Developed country    1
## 151              Denmark  Developed country    1
## 152   Dominican Republic  Developed country    1
## 153                Egypt  Developed country    1
## 154          El Salvador  Developed country    1
## 155              Equador  Developed country    1
## 156             Ethiopia  Developed country    1
## 157              Finland  Developed country    1
## 158               France  Developed country    1
## 159                Gabon  Developed country    1
## 160                Ghana  Developed country    1
## 161               Greece  Developed country    1
## 162            Guatemala  Developed country    1
## 163               Guinea  Developed country    1
## 164               Guyana  Developed country    1
## 165                Haiti  Developed country    1
## 166             Honduras  Developed country    1
## 167              Hungary  Developed country    1
## 168                India  Developed country    0
## 169            Indonesia  Developed country    1
## 170                 Iran  Developed country    1
## 171                 Iraq  Developed country    1
## 172              Ireland  Developed country    1
## 173               Israel  Developed country    0
## 174                Italy  Developed country    1
## 175              Jamaica  Developed country    1
## 176                Japan  Developed country    1
## 177               Jordan  Developed country    1
## 178                Kenya  Developed country    1
## 179               Kuwait  Developed country    1
## 180                 Laos  Developed country    1
## 181              Lebanon  Developed country    1
## 182              Lesotho  Developed country    1
## 183              Liberia  Developed country    1
## 184                Libya  Developed country    1
## 185           Madagascar  Developed country    1
## 186               Malawi  Developed country    1
## 187             Malaysia  Developed country    1
## 188                 Mali  Developed country    1
## 189           Mauritania  Developed country    1
## 190            Mauritius  Developed country    1
## 191               Mexico  Developed country    1
## 192             Mongolia  Developed country    0
## 193              Morocco  Developed country    1
## 194           Mozambique  Developed country    1
## 195              Myanmar  Developed country    1
## 196              Namibia  Developed country    0
## 197                Nepal  Developed country    1
## 198          Netherlands  Developed country    1
## 199          New Zealand  Developed country    1
## 200            Nicaragua  Developed country    1
## 201                Niger  Developed country    1
## 202              Nigeria  Developed country    1
## 203          North Korea  Developed country    1
## 204               Norway  Developed country    1
## 205                 Oman  Developed country    0
## 206             Pakistan  Developed country    0
## 207               Panama  Developed country    1
## 208     Papua New Guinea  Developed country    1
## 209             Paraguay  Developed country    1
## 210                 Peru  Developed country    1
## 211               Poland  Developed country    1
## 212             Portugal  Developed country    1
## 213              Romania  Developed country    0
## 214               Rwanda  Developed country    1
## 215         Saudi Arabia  Developed country    1
## 216              Senegal  Developed country    1
## 217         Sierra Leone  Developed country    1
## 218            Singapore  Developed country    1
## 219              Somalia  Developed country    1
## 220         South Africa  Developed country    0
## 221          South Korea  Developed country    1
## 222                Spain  Developed country    0
## 223            Sri Lanka  Developed country    1
## 224                Sudan  Developed country    1
## 225               Sweden  Developed country    0
## 226          Switzerland  Developed country    1
## 227                Syria  Developed country    1
## 228             Tanzania  Developed country    1
## 229             Thailand  Developed country    1
## 230                 Togo  Developed country    1
## 231    Trinidad & Tobago  Developed country    1
## 232              Tunisia  Developed country    1
## 233               Turkey  Developed country    1
## 234               Uganda  Developed country    1
## 235  United Arab Emirate  Developed country    1
## 236       United Kingdom  Developed country    1
## 237        United States  Developed country    1
## 238              Uruguay  Developed country    1
## 239            Venezuela  Developed country    1
## 240              Vietnam  Developed country    1
## 241                Yemen  Developed country    1
## 242                Zaire  Developed country    1
## 243               Zambia  Developed country    1
## 244             Zimbabwe  Developed country    1
summary(f)
##           Var1                     Var2          Freq    
##  Afghanistan:  2   Developing country:122   Min.   :0.0  
##  Albania    :  2   Developed country :122   1st Qu.:0.0  
##  Algeria    :  2                            Median :0.5  
##  Angola     :  2                            Mean   :0.5  
##  Argentina  :  2                            3rd Qu.:1.0  
##  Australia  :  2                            Max.   :1.0  
##  (Other)    :232

Bar diagram Study

Study about Country, region, populaton & Country type.

library(ggplot2)
library(plotly)
## Warning: package 'plotly' was built under R version 3.6.3
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
#Bar diagram between Country Vs Popn and Country Type
p<-ggplot(data=d, aes(x=country, 
                      y=pop92,
                      fill = develop
                      
))

p<-p + geom_bar(stat = "identity")
ggplotly(p)
#bar dagram of Region Population & Developing country
Q<-ggplot(data=d, aes(x=region, 
                      y=pop92,
                      fill = develop
                      
))

Q<-Q + geom_bar(stat = "identity")
ggplotly(Q)
#Bar diagran about country population and region
r<-ggplot(data=d, aes(x=country, 
                      y=pop92,
                      fill = region
                      
))

r<-r + geom_bar(stat = "identity")
ggplotly(r)

Study about Country, urban, Region and country Type

# bar diagram of Country, urban & Develop
a<-ggplot(data=d, aes(x=country, 
                      y=urban,
                      fill = develop
                      
))

a<-a + geom_bar(stat = "identity")
ggplotly(a)
#bar diagram about Regiion urban & Develop
b<-ggplot(data=d, aes(x=region, 
                      y=urban,
                      fill = develop
                      
))

b<-b + geom_bar(stat = "identity")
ggplotly(b)
#Bar diagram of Country , urban and region
c<-ggplot(data=d, aes(x=country, 
                      y=urban,
                      fill = region
                      
))

c<-c + geom_bar(stat = "identity")
ggplotly(c)

Study about country ,gdb, develop and Region

#bar diagram about country gdb and Develop
e<-ggplot(data=d, aes(x=country, 
                      y=gdp,
                      fill = develop
                      
))

e<-e + geom_bar(stat = "identity")
ggplotly(e)
#bar diagram about country, gdb and Region
f<-ggplot(data=d, aes(x=country, 
                      y=gdp,
                      fill = region
                      
))

f<-f + geom_bar(stat = "identity")
ggplotly(f)
#bar diagram about Region, gdb and Deveop
g<-ggplot(data=d, aes(x=region, 
                      y=gdp,
                      fill = develop
                      
))

g<-g + geom_bar(stat = "identity")
ggplotly(g)
#Study about country,life Expectency, Region & Develop
#bar diagram about country, lifeexpm and develop
h<-ggplot(data=d, aes(x=country, 
                      y=lifeexpm,
                      fill = develop
                      
))

h<-h + geom_bar(stat = "identity")
ggplotly(h)
#bar diagram about country, lifeexpm and Region
i<-ggplot(data=d, aes(x=country, 
                      y=lifeexpm,
                      fill = region
                      
))

i<-i + geom_bar(stat = "identity")
ggplotly(i)
#bar diagram about Region, Lifeexpm and Develop
j<-ggplot(data=d, aes(x=region, 
                      y=lifeexpm,
                      fill = develop
                      
))

j<-j + geom_bar(stat = "identity")
ggplotly(j)
#Country birthrate and region
k<-ggplot(data=d, aes(x=country, 
                      y=birthrat,
                      fill = develop
                      
))

k<-k + geom_bar(stat = "identity")
ggplotly(k)
## Warning: Removed 1 rows containing missing values (position_stack).
#Country Death rate and Region
l<-ggplot(data=d, aes(x=country, 
                      y=deathrat,
                      fill = develop
                      
))

l<-l + geom_bar(stat = "identity")
ggplotly(l)
## Warning: Removed 1 rows containing missing values (position_stack).
#Country Infant ortality & Develop
m<-ggplot(data=d, aes(x=country, 
                      y=infmr,
                      fill = develop
                      
))

m<-m + geom_bar(stat = "identity")
ggplotly(m)
#Country Fertility rate & Develop
n<-ggplot(data=d, aes(x=country, 
                      y=fertrate,
                      fill = develop
                      
))

n<-n + geom_bar(stat = "identity")
ggplotly(n)
## Warning: Removed 2 rows containing missing values (position_stack).
#Bar diagram about Country Birth rate Death rate & Fertillity
o<-ggplot(data=d, aes(x=birthrat, 
                      y=deathrat,
                      fill = country
                      
))

o<-o + geom_bar(stat = "identity")
ggplotly(o)
## Warning: Removed 1 rows containing missing values (position_stack).

Analysis Part

reg<-lm(formula = gdp~pop92+urban+lifeexpm+lifeexpf+birthrat+deathrat+infmr+fertrate,data = d)
reg
## 
## Call:
## lm(formula = gdp ~ pop92 + urban + lifeexpm + lifeexpf + birthrat + 
##     deathrat + infmr + fertrate, data = d)
## 
## Coefficients:
## (Intercept)        pop92        urban     lifeexpm     lifeexpf     birthrat  
##  -4.054e+04    1.841e-01    4.686e+01    3.488e+02    1.972e+02   -1.933e+02  
##    deathrat        infmr     fertrate  
##   1.007e+03   -1.428e+01    9.519e+02
summary(reg)
## 
## Call:
## lm(formula = gdp ~ pop92 + urban + lifeexpm + lifeexpf + birthrat + 
##     deathrat + infmr + fertrate, data = d)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -11265.5  -1517.9    232.7   1728.9  10679.7 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -4.054e+04  1.203e+04  -3.371  0.00103 ** 
## pop92        1.841e-01  2.523e+00   0.073  0.94195    
## urban        4.686e+01  1.963e+01   2.387  0.01871 *  
## lifeexpm     3.488e+02  2.304e+02   1.514  0.13281    
## lifeexpf     1.972e+02  2.392e+02   0.825  0.41135    
## birthrat    -1.933e+02  9.528e+01  -2.029  0.04487 *  
## deathrat     1.007e+03  1.391e+02   7.242  6.4e-11 ***
## infmr       -1.428e+01  3.131e+01  -0.456  0.64919    
## fertrate     9.519e+02  5.706e+02   1.668  0.09814 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3489 on 110 degrees of freedom
##   (3 observations deleted due to missingness)
## Multiple R-squared:  0.6913, Adjusted R-squared:  0.6688 
## F-statistic: 30.78 on 8 and 110 DF,  p-value: < 2.2e-16

From the above ANNOVA table we find that, Urbanization birthrate are Singnificant, on the other hand Deathrate are Highly Significant of the dependent variable GDP. Adjusted R squared is 0.6688, that means the total 67 percent can be explained of the independent variable GDP.

Regression line & Scatter diagram

#Regession Line of GDP, Birthrate among Developed & Developing Countries
d1<-ggplot(
  data = d, 
  aes(x = gdp, y = birthrat,fil=develop,colour=develop)) +
  geom_point() + 
  geom_smooth(method = "lm") +
  ggtitle("gdp vs. birthrat & develop") +
  xlab("gdp") +
  ylab("birthrat")
ggplotly(d1)
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
#Regression line of GDP, Deathrate among Developed & Developing Countries
d2<-ggplot(
  data = d, 
  aes(x = gdp, y = deathrat,fil=develop,colour=develop)) +
  geom_point() + 
  geom_smooth(method = "lm") +
  ggtitle("gdp vs. deathrat & develop") +
  xlab("gdp") +
  ylab("deathrat")
ggplotly(d2)
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
#Regression ine of GDP, Urbanaization among Developed and Developing Countries
d3<-ggplot(
  data = d, 
  aes(x = gdp, y = urban,fil=develop,colour=develop)) +
  geom_point() + 
  geom_smooth(method = "lm") +
  ggtitle("gdp vs. urban & develop") +
  xlab("gdp") +
  ylab("urban")
ggplotly(d3)

Decesion Trees

library(rpart)
library(rpart.plot)
## Warning: package 'rpart.plot' was built under R version 3.6.1
latlontree1 = rpart(pop92~gdp+lifeexpm,data=d)
latlontree2 = rpart(pop92~gdp+birthrat,data=d)
latlontree3 = rpart(pop92~gdp+deathrat,data=d)
# Plot the tree using prp command defined in rpart.plot package
prp(latlontree1)

prp(latlontree2)

prp(latlontree3)

tree1 = rpart(pop92~gdp+urban+lifeexpm,data=d)
tree2 = rpart(pop92~gdp+birthrat,data=d)
tree3 = rpart(pop92~gdp+deathrat,data=d)
rpart.plot(tree1)

rpart.plot(tree2)

rpart.plot(tree3)

Regional Observation & Histogram

#Getting Region Developed 
library(tidyverse)
## Warning: package 'tidyverse' was built under R version 3.6.1
## -- Attaching packages ------------------------------------------------------------------------ tidyverse 1.2.1 --
## v tibble  2.1.3     v purrr   0.3.3
## v tidyr   1.0.0     v dplyr   0.8.3
## v readr   1.3.1     v stringr 1.4.0
## v tibble  2.1.3     v forcats 0.4.0
## Warning: package 'tibble' was built under R version 3.6.1
## Warning: package 'tidyr' was built under R version 3.6.1
## Warning: package 'readr' was built under R version 3.6.1
## Warning: package 'purrr' was built under R version 3.6.1
## Warning: package 'dplyr' was built under R version 3.6.1
## Warning: package 'stringr' was built under R version 3.6.1
## Warning: package 'forcats' was built under R version 3.6.1
## -- Conflicts --------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks plotly::filter(), stats::filter()
## x dplyr::first()  masks xts::first()
## x dplyr::lag()    masks stats::lag()
## x dplyr::last()   masks xts::last()
library(dplyr)
library(tidyr)
library(PerformanceAnalytics)
table<-d %>% 
     select(country = country, gdp, develop,region,pop92,birthrat) %>%
    group_by(country, develop,region,pop92,birthrat) %>%
     summarise(total_gdp = sum(gdp))
data.frame(table)
##                  country            develop          region    pop92 birthrat
## 1            Afghanistan  Developed country    Western Asia   16.095     44.0
## 2                Albania Developing country  Western Europe    3.285     23.0
## 3                Algeria  Developed country Southern Africa   26.666     31.0
## 4                 Angola  Developed country Northern Africa    8.902     46.0
## 5              Argentina Developing country   North America   32.901     20.0
## 6              Australia  Developed country            USSR   17.567     15.0
## 7                Austria  Developed country         Oceania    7.867     12.0
## 8             Bangladesh Developing country    Western Asia  119.000     36.0
## 9                Belgium  Developed country         Oceania   10.016     10.0
## 10                 Benin  Developed country       Caribbean    4.997     49.0
## 11                Bhutan  Developed country    Western Asia    1.660     40.0
## 12               Bolivia  Developed country   North America    7.323     34.0
## 13              Botswana  Developed country  Western Africa    1.300     36.0
## 14                Brazil  Developed country   North America  158.000     26.0
## 15              Bulgaria Developing country Northern Europe    8.868     13.0
## 16          Burkina Faso  Developed country       Caribbean    9.653     50.0
## 17               Burundi  Developed country   Middle Africa    6.022     46.0
## 18              Cambodia  Developed country   Southern Asia    7.249     37.0
## 19              Cameroon  Developed country Northern Africa   12.658     44.0
## 20                Canada  Developed country    Eastern Asia   27.351     14.0
## 21  Central African Rep.  Developed country Northern Africa    3.029     44.0
## 22                  Chad  Developed country Northern Africa    5.238     42.0
## 23                 Chile  Developed country   North America   13.528     21.0
## 24                 China  Developed country  Southeast Asia 1169.619     22.0
## 25              Colombia  Developed country   North America   34.296     24.0
## 26                 Congo  Developed country Northern Africa    2.376     43.0
## 27            Costa Rica  Developed country   South America    3.187     27.0
## 28                  Cuba  Developed country Central America   10.846     18.0
## 29               Denmark  Developed country Southern Europe    5.163     12.0
## 30    Dominican Republic  Developed country Central America    7.515     27.0
## 31                 Egypt  Developed country Southern Africa   56.386     33.0
## 32           El Salvador  Developed country   South America    5.574     33.0
## 33               Equador  Developed country   North America   10.933     28.0
## 34              Ethiopia  Developed country   Middle Africa   51.070     45.0
## 35               Finland  Developed country Southern Europe    5.004     12.0
## 36                France  Developed country         Oceania   57.287     13.0
## 37                 Gabon  Developed country Northern Africa    1.106     28.0
## 38                 Ghana  Developed country       Caribbean   16.185     46.0
## 39                Greece  Developed country  Western Europe   10.064     11.0
## 40             Guatemala  Developed country   South America    9.784     34.0
## 41                Guinea  Developed country       Caribbean    7.783     46.0
## 42                Guyana  Developed country   North America    0.739     21.0
## 43                 Haiti  Developed country Central America    6.431     43.0
## 44              Honduras  Developed country   South America    4.949     38.0
## 45               Hungary  Developed country Northern Europe   10.333     12.0
## 46                 India Developing country    Western Asia  886.362     30.0
## 47             Indonesia  Developed country   Southern Asia  195.000     26.0
## 48                  Iran  Developed country    Western Asia   61.183     44.0
## 49                  Iraq  Developed country  Eastern Europe   18.445     45.0
## 50               Ireland  Developed country Southern Europe    3.531     15.0
## 51                Israel Developing country  Eastern Europe    4.748     21.0
## 52                 Italy  Developed country  Western Europe   57.904     11.0
## 53               Jamaica  Developed country Central America    2.506     24.0
## 54                 Japan  Developed country  Southeast Asia  124.460     10.0
## 55                Jordan  Developed country  Eastern Europe    3.557     46.7
## 56                 Kenya  Developed country   Middle Africa   26.164     44.0
## 57                Kuwait  Developed country  Eastern Europe    1.318     32.0
## 58                  Laos  Developed country   Southern Asia    4.440     44.0
## 59               Lebanon  Developed country  Eastern Europe    3.439     28.0
## 60               Lesotho  Developed country  Western Africa    1.848     35.0
## 61               Liberia  Developed country       Caribbean    2.462     45.0
## 62                 Libya  Developed country Southern Africa   44.850     36.0
## 63            Madagascar  Developed country   Middle Africa   12.596     47.0
## 64                Malawi  Developed country   Middle Africa    9.605     52.0
## 65              Malaysia  Developed country   Southern Asia   18.410     30.0
## 66                  Mali  Developed country       Caribbean    8.641     51.0
## 67            Mauritania  Developed country       Caribbean    2.059     49.0
## 68             Mauritius  Developed country   Middle Africa    1.082     19.0
## 69                Mexico  Developed country   South America   92.380     29.0
## 70              Mongolia Developing country  Southeast Asia    2.305     34.0
## 71               Morocco  Developed country Southern Africa   26.708     30.0
## 72            Mozambique  Developed country   Middle Africa   15.469     46.0
## 73               Myanmar  Developed country   Southern Asia   42.642     29.0
## 74               Namibia Developing country  Western Africa    1.574     45.0
## 75                 Nepal  Developed country    Western Asia   20.086     38.0
## 76           Netherlands  Developed country         Oceania   15.112     13.0
## 77           New Zealand  Developed country            USSR    3.347     15.0
## 78             Nicaragua  Developed country   South America    3.878     37.0
## 79                 Niger  Developed country       Caribbean    8.052     58.0
## 80               Nigeria  Developed country       Caribbean   88.500     46.0
## 81           North Korea  Developed country  Southeast Asia   22.227     24.0
## 82                Norway  Developed country Southern Europe    4.294     14.0
## 83                  Oman Developing country  Eastern Europe    1.587       NA
## 84              Pakistan Developing country    Western Asia  121.644     43.0
## 85                Panama  Developed country   South America    2.529     26.0
## 86      Papua New Guinea  Developed country            USSR    4.006     34.0
## 87              Paraguay  Developed country   North America    4.929     33.0
## 88                  Peru  Developed country   North America   22.767     28.0
## 89                Poland  Developed country Northern Europe   38.385     14.0
## 90              Portugal  Developed country  Western Europe   10.448     12.0
## 91               Romania Developing country Northern Europe   23.169     14.0
## 92                Rwanda  Developed country   Middle Africa    8.206     52.0
## 93          Saudi Arabia  Developed country  Eastern Europe   16.900     38.0
## 94               Senegal  Developed country       Caribbean    8.205     44.0
## 95          Sierra Leone  Developed country       Caribbean    4.456     46.0
## 96             Singapore  Developed country   Southern Asia    2.792     18.0
## 97               Somalia  Developed country   Middle Africa    7.235     46.0
## 98          South Africa Developing country  Western Africa   41.688     35.0
## 99           South Korea  Developed country  Southeast Asia   44.149     15.0
## 100                Spain Developing country  Western Europe   39.118     11.0
## 101            Sri Lanka  Developed country    Western Asia   17.631     21.0
## 102                Sudan  Developed country Southern Africa   28.305     44.0
## 103               Sweden Developing country Southern Europe    8.602     13.0
## 104          Switzerland  Developed country         Oceania    6.828     12.0
## 105                Syria  Developed country  Eastern Europe   13.730     44.0
## 106             Tanzania  Developed country   Middle Africa   27.791     50.0
## 107             Thailand  Developed country   Southern Asia   57.624     20.0
## 108                 Togo  Developed country       Caribbean    3.958     48.0
## 109    Trinidad & Tobago  Developed country Central America    1.285     21.0
## 110              Tunisia  Developed country Southern Africa    8.445     26.0
## 111               Turkey  Developed country  Eastern Europe   59.640     28.0
## 112               Uganda  Developed country  Eastern Africa   19.386     51.0
## 113  United Arab Emirate  Developed country  Eastern Europe    2.522     20.0
## 114       United Kingdom  Developed country Southern Europe   57.798     14.0
## 115        United States  Developed country    Eastern Asia  256.561     14.0
## 116              Uruguay  Developed country   North America    3.121     17.0
## 117            Venezuela  Developed country   North America   20.675     28.0
## 118              Vietnam  Developed country   Southern Asia   68.964     29.0
## 119                Yemen  Developed country  Eastern Europe   10.394     51.0
## 120                Zaire  Developed country Northern Africa   39.084     45.0
## 121               Zambia  Developed country   Middle Africa    8.745     48.0
## 122             Zimbabwe  Developed country   Middle Africa   11.033     41.0
##     total_gdp
## 1         220
## 2        1300
## 3        2130
## 4         950
## 5        3100
## 6       18054
## 7       20895
## 8         200
## 9       17300
## 10        400
## 11        199
## 12        690
## 13       2384
## 14       2540
## 15       5300
## 16        205
## 17        200
## 18        130
## 19       1010
## 20      19400
## 21        440
## 22        190
## 23       2200
## 24        360
## 25       1300
## 26       1070
## 27       1810
## 28       1580
## 29      17700
## 30        950
## 31        720
## 32       1010
## 33       1070
## 34        130
## 35      16200
## 36      18300
## 37       4400
## 38        400
## 39       7730
## 40       1260
## 41        410
## 42        300
## 43        440
## 44        960
## 45       5700
## 46        380
## 47        630
## 48       1500
## 49       1950
## 50      11200
## 51      12500
## 52      16700
## 53       1400
## 54      19100
## 55       1012
## 56        385
## 57       6200
## 58        200
## 59       1400
## 60        240
## 61        440
## 62       6800
## 63        200
## 64        200
## 65       2670
## 66        265
## 67        520
## 68       2300
## 69       3200
## 70        900
## 71       1060
## 72        120
## 73        530
## 74       1400
## 75        165
## 76      16600
## 77      14000
## 78        425
## 79        300
## 80        230
## 81       1035
## 82      17100
## 83       4852
## 84        380
## 85       1150
## 86        800
## 87       1460
## 88        920
## 89       4300
## 90       8400
## 91       3100
## 92        310
## 93       5800
## 94        615
## 95        330
## 96      13900
## 97        170
## 98       2600
## 99       6300
## 100     12400
## 101       410
## 102       450
## 103     17200
## 104     21700
## 105      2300
## 106       260
## 107      1630
## 108       400
## 109      3600
## 110      1320
## 111      3400
## 112       300
## 113     12100
## 114     15900
## 115     22470
## 116      2935
## 117      2590
## 118       230
## 119       545
## 120       180
## 121       380
## 122       660
#Western Asia Observation
w<-d%>%filter(country==country, region=="Western Asia")%>%
  group_by(country,pop92,urban,lifeexpm,lifeexpf,birthrat,deathrat,infmr,fertrate,develop)%>%summarise(gdp=sum(gdp))%>%ungroup()
data.frame(w)
##       country   pop92 urban lifeexpm lifeexpf birthrat deathrat infmr fertrate
## 1 Afghanistan  16.095    18       45       43       44       20   164      6.8
## 2  Bangladesh 119.000    24       55       54       36       13   112      5.1
## 3      Bhutan   1.660    95       50       48       40       17   126      5.5
## 4       India 886.362    28       57       58       30       11    81      4.1
## 5        Iran  61.183    57       64       66       44       10    66      5.4
## 6       Nepal  20.086     8       51       51       38       14    90      5.5
## 7    Pakistan 121.644    32       56       57       43       14   105      5.9
## 8   Sri Lanka  17.631    26       69       74       21        6    21      2.5
##              develop  gdp
## 1  Developed country  220
## 2 Developing country  200
## 3  Developed country  199
## 4 Developing country  380
## 5  Developed country 1500
## 6  Developed country  165
## 7 Developing country  380
## 8  Developed country  410
summary(w)
##         country      pop92            urban          lifeexpm    
##  Afghanistan:1   Min.   :  1.66   Min.   : 8.00   Min.   :45.00  
##  Bangladesh :1   1st Qu.: 17.25   1st Qu.:22.50   1st Qu.:50.75  
##  Bhutan     :1   Median : 40.63   Median :27.00   Median :55.50  
##  India      :1   Mean   :155.46   Mean   :36.00   Mean   :55.88  
##  Iran       :1   3rd Qu.:119.66   3rd Qu.:38.25   3rd Qu.:58.75  
##  Nepal      :1   Max.   :886.36   Max.   :95.00   Max.   :69.00  
##  (Other)    :2                                                   
##     lifeexpf        birthrat        deathrat         infmr       
##  Min.   :43.00   Min.   :21.00   Min.   : 6.00   Min.   : 21.00  
##  1st Qu.:50.25   1st Qu.:34.50   1st Qu.:10.75   1st Qu.: 77.25  
##  Median :55.50   Median :39.00   Median :13.50   Median : 97.50  
##  Mean   :56.38   Mean   :37.00   Mean   :13.12   Mean   : 95.62  
##  3rd Qu.:60.00   3rd Qu.:43.25   3rd Qu.:14.75   3rd Qu.:115.50  
##  Max.   :74.00   Max.   :44.00   Max.   :20.00   Max.   :164.00  
##                                                                  
##     fertrate                  develop       gdp        
##  Min.   :2.50   Developing country:3   Min.   : 165.0  
##  1st Qu.:4.85   Developed country :5   1st Qu.: 199.8  
##  Median :5.45                          Median : 300.0  
##  Mean   :5.10                          Mean   : 431.8  
##  3rd Qu.:5.60                          3rd Qu.: 387.5  
##  Max.   :6.80                          Max.   :1500.0  
## 
#Different Histogram of Westen Asia
library(PerformanceAnalytics)
chart.Correlation(w[,2:9,11], histogram=TRUE, pch=19)

#Western Europe Observation
we<-d%>%filter(country==country, region=="Western Europe")%>%
  group_by(country,pop92,urban,lifeexpm,lifeexpf,birthrat,deathrat,infmr,fertrate,develop)%>%summarise(gdp=sum(gdp))%>%ungroup()
data.frame(we)
##    country  pop92 urban lifeexpm lifeexpf birthrat deathrat infmr fertrate
## 1  Albania  3.285    35       72       79       23        5    27      2.7
## 2   Greece 10.064    63       75       80       11        9    10      1.7
## 3    Italy 57.904    67       74       81       11       10     8      1.5
## 4 Portugal 10.448    34       71       78       12       10    10      1.7
## 5    Spain 39.118    79       75       82       11        8     6      1.7
##              develop   gdp
## 1 Developing country  1300
## 2  Developed country  7730
## 3  Developed country 16700
## 4  Developed country  8400
## 5 Developing country 12400
summary(we)
##         country      pop92            urban         lifeexpm       lifeexpf 
##  Albania    :1   Min.   : 3.285   Min.   :34.0   Min.   :71.0   Min.   :78  
##  Greece     :1   1st Qu.:10.064   1st Qu.:35.0   1st Qu.:72.0   1st Qu.:79  
##  Italy      :1   Median :10.448   Median :63.0   Median :74.0   Median :80  
##  Portugal   :1   Mean   :24.164   Mean   :55.6   Mean   :73.4   Mean   :80  
##  Spain      :1   3rd Qu.:39.118   3rd Qu.:67.0   3rd Qu.:75.0   3rd Qu.:81  
##  Afghanistan:0   Max.   :57.904   Max.   :79.0   Max.   :75.0   Max.   :82  
##  (Other)    :0                                                              
##     birthrat       deathrat        infmr         fertrate   
##  Min.   :11.0   Min.   : 5.0   Min.   : 6.0   Min.   :1.50  
##  1st Qu.:11.0   1st Qu.: 8.0   1st Qu.: 8.0   1st Qu.:1.70  
##  Median :11.0   Median : 9.0   Median :10.0   Median :1.70  
##  Mean   :13.6   Mean   : 8.4   Mean   :12.2   Mean   :1.86  
##  3rd Qu.:12.0   3rd Qu.:10.0   3rd Qu.:10.0   3rd Qu.:1.70  
##  Max.   :23.0   Max.   :10.0   Max.   :27.0   Max.   :2.70  
##                                                             
##                develop       gdp       
##  Developing country:2   Min.   : 1300  
##  Developed country :3   1st Qu.: 7730  
##                         Median : 8400  
##                         Mean   : 9306  
##                         3rd Qu.:12400  
##                         Max.   :16700  
## 
#Different Histogram
chart.Correlation(we[,2:9,11], histogram=TRUE, pch=19)

#Southern Africa Observation
Sa<-d%>%filter(country==country, region=="Southern Africa")%>%
  group_by(country,pop92,urban,lifeexpm,lifeexpf,birthrat,deathrat,infmr,fertrate,develop)%>%summarise(gdp=sum(gdp))%>%ungroup()
data.frame(Sa)
##   country  pop92 urban lifeexpm lifeexpf birthrat deathrat infmr fertrate
## 1 Algeria 26.666    51       66       68       31        7    57      5.2
## 2   Egypt 56.386    44       58       62       33       10    80      4.2
## 3   Libya 44.850    70       66       71       36        6    60      6.7
## 4 Morocco 26.708    50       63       67       30        8    56      4.2
## 5   Sudan 28.305    22       53       54       44       14    85      6.3
## 6 Tunisia  8.445    49       70       74       26        5    38      3.4
##             develop  gdp
## 1 Developed country 2130
## 2 Developed country  720
## 3 Developed country 6800
## 4 Developed country 1060
## 5 Developed country  450
## 6 Developed country 1320
summary(Sa)
##     country      pop92            urban          lifeexpm        lifeexpf    
##  Algeria:1   Min.   : 8.445   Min.   :22.00   Min.   :53.00   Min.   :54.00  
##  Egypt  :1   1st Qu.:26.677   1st Qu.:45.25   1st Qu.:59.25   1st Qu.:63.25  
##  Libya  :1   Median :27.506   Median :49.50   Median :64.50   Median :67.50  
##  Morocco:1   Mean   :31.893   Mean   :47.67   Mean   :62.67   Mean   :66.00  
##  Sudan  :1   3rd Qu.:40.714   3rd Qu.:50.75   3rd Qu.:66.00   3rd Qu.:70.25  
##  Tunisia:1   Max.   :56.386   Max.   :70.00   Max.   :70.00   Max.   :74.00  
##  (Other):0                                                                   
##     birthrat        deathrat          infmr          fertrate    
##  Min.   :26.00   Min.   : 5.000   Min.   :38.00   Min.   :3.400  
##  1st Qu.:30.25   1st Qu.: 6.250   1st Qu.:56.25   1st Qu.:4.200  
##  Median :32.00   Median : 7.500   Median :58.50   Median :4.700  
##  Mean   :33.33   Mean   : 8.333   Mean   :62.67   Mean   :5.000  
##  3rd Qu.:35.25   3rd Qu.: 9.500   3rd Qu.:75.00   3rd Qu.:6.025  
##  Max.   :44.00   Max.   :14.000   Max.   :85.00   Max.   :6.700  
##                                                                  
##                develop       gdp      
##  Developing country:0   Min.   : 450  
##  Developed country :6   1st Qu.: 805  
##                         Median :1190  
##                         Mean   :2080  
##                         3rd Qu.:1928  
##                         Max.   :6800  
## 
#Different Histogram
chart.Correlation(Sa[,2:9,11], histogram=TRUE, pch=19)

#Northern Africa observation
Na<-d%>%filter(country==country, region=="Northern Africa")%>%
  group_by(country,pop92,urban,lifeexpm,lifeexpf,birthrat,deathrat,infmr,fertrate,develop)%>%summarise(gdp=sum(gdp))%>%ungroup()
data.frame(Na)
##                country  pop92 urban lifeexpm lifeexpf birthrat deathrat infmr
## 1               Angola  8.902    29       43       47       46       19   151
## 2             Cameroon 12.658    40       55       60       44       11    81
## 3 Central African Rep.  3.029    47       46       49       44       19   135
## 4                 Chad  5.238    32       39       41       42       22   136
## 5                Congo  2.376    41       52       56       43       13   110
## 6                Gabon  1.106    45       51       56       28       15   100
## 7                Zaire 39.084    44       52       56       45       14    97
##   fertrate           develop  gdp
## 1      6.3 Developed country  950
## 2      5.7 Developed country 1010
## 3      5.8 Developed country  440
## 4      5.8 Developed country  190
## 5      5.8 Developed country 1070
## 6      5.3 Developed country 4400
## 7      6.0 Developed country  180
summary(Na)
##                  country      pop92            urban          lifeexpm    
##  Angola              :1   Min.   : 1.106   Min.   :29.00   Min.   :39.00  
##  Cameroon            :1   1st Qu.: 2.703   1st Qu.:36.00   1st Qu.:44.50  
##  Central African Rep.:1   Median : 5.238   Median :41.00   Median :51.00  
##  Chad                :1   Mean   :10.342   Mean   :39.71   Mean   :48.29  
##  Congo               :1   3rd Qu.:10.780   3rd Qu.:44.50   3rd Qu.:52.00  
##  Gabon               :1   Max.   :39.084   Max.   :47.00   Max.   :55.00  
##  (Other)             :1                                                   
##     lifeexpf        birthrat        deathrat         infmr      
##  Min.   :41.00   Min.   :28.00   Min.   :11.00   Min.   : 81.0  
##  1st Qu.:48.00   1st Qu.:42.50   1st Qu.:13.50   1st Qu.: 98.5  
##  Median :56.00   Median :44.00   Median :15.00   Median :110.0  
##  Mean   :52.14   Mean   :41.71   Mean   :16.14   Mean   :115.7  
##  3rd Qu.:56.00   3rd Qu.:44.50   3rd Qu.:19.00   3rd Qu.:135.5  
##  Max.   :60.00   Max.   :46.00   Max.   :22.00   Max.   :151.0  
##                                                                 
##     fertrate                   develop       gdp      
##  Min.   :5.300   Developing country:0   Min.   : 180  
##  1st Qu.:5.750   Developed country :7   1st Qu.: 315  
##  Median :5.800                          Median : 950  
##  Mean   :5.814                          Mean   :1177  
##  3rd Qu.:5.900                          3rd Qu.:1040  
##  Max.   :6.300                          Max.   :4400  
## 
#Different Histogram
chart.Correlation(Na[,2:9,11], histogram=TRUE, pch=19)

#North America observation
Nu<-d%>%filter(country==country, region=="North America")%>%
  group_by(country,pop92,urban,lifeexpm,lifeexpf,birthrat,deathrat,infmr,fertrate,develop)%>%summarise(gdp=sum(gdp))%>%ungroup()
data.frame(Nu)
##      country   pop92 urban lifeexpm lifeexpf birthrat deathrat infmr fertrate
## 1  Argentina  32.901  87.0       67       74       20        9    31      2.8
## 2    Bolivia   7.323  51.0       59       64       34       19    83      5.8
## 3     Brazil 158.000  76.0       62       68       26        7    67      3.2
## 4      Chile  13.528  84.0       71       77       21        6    18      2.7
## 5   Colombia  34.296  65.4       69       74       24        5    37      3.3
## 6    Equador  10.933  54.0       67       72       28        6    60      4.3
## 7     Guyana   0.739  35.0       61       68       21        7    51      2.4
## 8   Paraguay   4.929  46.0       71       74       33        5    28      4.3
## 9       Peru  22.767  70.0       63       67       28        8    59      4.0
## 10   Uruguay   3.121  86.0       69       76       17       10    22      2.5
## 11 Venezuela  20.675  83.0       71       78       28        4    23      3.5
##               develop  gdp
## 1  Developing country 3100
## 2   Developed country  690
## 3   Developed country 2540
## 4   Developed country 2200
## 5   Developed country 1300
## 6   Developed country 1070
## 7   Developed country  300
## 8   Developed country 1460
## 9   Developed country  920
## 10  Developed country 2935
## 11  Developed country 2590
summary(Nu)
##       country      pop92             urban          lifeexpm        lifeexpf 
##  Argentina:1   Min.   :  0.739   Min.   :35.00   Min.   :59.00   Min.   :64  
##  Bolivia  :1   1st Qu.:  6.126   1st Qu.:52.50   1st Qu.:62.50   1st Qu.:68  
##  Brazil   :1   Median : 13.528   Median :70.00   Median :67.00   Median :74  
##  Chile    :1   Mean   : 28.110   Mean   :67.04   Mean   :66.36   Mean   :72  
##  Colombia :1   3rd Qu.: 27.834   3rd Qu.:83.50   3rd Qu.:70.00   3rd Qu.:75  
##  Equador  :1   Max.   :158.000   Max.   :87.00   Max.   :71.00   Max.   :78  
##  (Other)  :5                                                                 
##     birthrat        deathrat          infmr          fertrate    
##  Min.   :17.00   Min.   : 4.000   Min.   :18.00   Min.   :2.400  
##  1st Qu.:21.00   1st Qu.: 5.500   1st Qu.:25.50   1st Qu.:2.750  
##  Median :26.00   Median : 7.000   Median :37.00   Median :3.300  
##  Mean   :25.45   Mean   : 7.818   Mean   :43.55   Mean   :3.527  
##  3rd Qu.:28.00   3rd Qu.: 8.500   3rd Qu.:59.50   3rd Qu.:4.150  
##  Max.   :34.00   Max.   :19.000   Max.   :83.00   Max.   :5.800  
##                                                                  
##                develop        gdp      
##  Developing country: 1   Min.   : 300  
##  Developed country :10   1st Qu.: 995  
##                          Median :1460  
##                          Mean   :1737  
##                          3rd Qu.:2565  
##                          Max.   :3100  
## 
#Different Histogram
chart.Correlation(Nu[,2:9,11], histogram=TRUE, pch=19)

#USSR Observation
Uss<-d%>%filter(country==country, region=="USSR")%>%
  group_by(country,pop92,urban,lifeexpm,lifeexpf,birthrat,deathrat,infmr,fertrate,develop)%>%summarise(gdp=sum(gdp))%>%ungroup()
data.frame(Uss)
##            country  pop92 urban lifeexpm lifeexpf birthrat deathrat infmr
## 1        Australia 17.567    85       74       80       15        8     8
## 2      New Zealand  3.347    76       72       80       15        8    10
## 3 Papua New Guinea  4.006    15       55       56       34       11    55
##   fertrate           develop   gdp
## 1      1.8 Developed country 18054
## 2      1.8 Developed country 14000
## 3       NA Developed country   800
summary(Uss)
##              country      pop92            urban          lifeexpm   
##  Australia       :1   Min.   : 3.347   Min.   :15.00   Min.   :55.0  
##  New Zealand     :1   1st Qu.: 3.676   1st Qu.:45.50   1st Qu.:63.5  
##  Papua New Guinea:1   Median : 4.006   Median :76.00   Median :72.0  
##  Afghanistan     :0   Mean   : 8.307   Mean   :58.67   Mean   :67.0  
##  Albania         :0   3rd Qu.:10.787   3rd Qu.:80.50   3rd Qu.:73.0  
##  Algeria         :0   Max.   :17.567   Max.   :85.00   Max.   :74.0  
##  (Other)         :0                                                  
##     lifeexpf     birthrat        deathrat        infmr          fertrate  
##  Min.   :56   Min.   :15.00   Min.   : 8.0   Min.   : 8.00   Min.   :1.8  
##  1st Qu.:68   1st Qu.:15.00   1st Qu.: 8.0   1st Qu.: 9.00   1st Qu.:1.8  
##  Median :80   Median :15.00   Median : 8.0   Median :10.00   Median :1.8  
##  Mean   :72   Mean   :21.33   Mean   : 9.0   Mean   :24.33   Mean   :1.8  
##  3rd Qu.:80   3rd Qu.:24.50   3rd Qu.: 9.5   3rd Qu.:32.50   3rd Qu.:1.8  
##  Max.   :80   Max.   :34.00   Max.   :11.0   Max.   :55.00   Max.   :1.8  
##                                                              NA's   :1    
##                develop       gdp       
##  Developing country:0   Min.   :  800  
##  Developed country :3   1st Qu.: 7400  
##                         Median :14000  
##                         Mean   :10951  
##                         3rd Qu.:16027  
##                         Max.   :18054  
## 
#Oceania observation
Oc<-d%>%filter(country==country, region=="USSR")%>%
  group_by(country,pop92,urban,lifeexpm,lifeexpf,birthrat,deathrat,infmr,fertrate,develop)%>%summarise(gdp=sum(gdp))%>%ungroup()
data.frame(Oc)
##            country  pop92 urban lifeexpm lifeexpf birthrat deathrat infmr
## 1        Australia 17.567    85       74       80       15        8     8
## 2      New Zealand  3.347    76       72       80       15        8    10
## 3 Papua New Guinea  4.006    15       55       56       34       11    55
##   fertrate           develop   gdp
## 1      1.8 Developed country 18054
## 2      1.8 Developed country 14000
## 3       NA Developed country   800
summary(Oc)
##              country      pop92            urban          lifeexpm   
##  Australia       :1   Min.   : 3.347   Min.   :15.00   Min.   :55.0  
##  New Zealand     :1   1st Qu.: 3.676   1st Qu.:45.50   1st Qu.:63.5  
##  Papua New Guinea:1   Median : 4.006   Median :76.00   Median :72.0  
##  Afghanistan     :0   Mean   : 8.307   Mean   :58.67   Mean   :67.0  
##  Albania         :0   3rd Qu.:10.787   3rd Qu.:80.50   3rd Qu.:73.0  
##  Algeria         :0   Max.   :17.567   Max.   :85.00   Max.   :74.0  
##  (Other)         :0                                                  
##     lifeexpf     birthrat        deathrat        infmr          fertrate  
##  Min.   :56   Min.   :15.00   Min.   : 8.0   Min.   : 8.00   Min.   :1.8  
##  1st Qu.:68   1st Qu.:15.00   1st Qu.: 8.0   1st Qu.: 9.00   1st Qu.:1.8  
##  Median :80   Median :15.00   Median : 8.0   Median :10.00   Median :1.8  
##  Mean   :72   Mean   :21.33   Mean   : 9.0   Mean   :24.33   Mean   :1.8  
##  3rd Qu.:80   3rd Qu.:24.50   3rd Qu.: 9.5   3rd Qu.:32.50   3rd Qu.:1.8  
##  Max.   :80   Max.   :34.00   Max.   :11.0   Max.   :55.00   Max.   :1.8  
##                                                              NA's   :1    
##                develop       gdp       
##  Developing country:0   Min.   :  800  
##  Developed country :3   1st Qu.: 7400  
##                         Median :14000  
##                         Mean   :10951  
##                         3rd Qu.:16027  
##                         Max.   :18054  
## 
#Caribbean observation
Ca<-d%>%filter(country==country, region=="USSR")%>%
  group_by(country,pop92,urban,lifeexpm,lifeexpf,birthrat,deathrat,infmr,fertrate,develop)%>%summarise(gdp=sum(gdp))%>%ungroup()
data.frame(Ca)
##            country  pop92 urban lifeexpm lifeexpf birthrat deathrat infmr
## 1        Australia 17.567    85       74       80       15        8     8
## 2      New Zealand  3.347    76       72       80       15        8    10
## 3 Papua New Guinea  4.006    15       55       56       34       11    55
##   fertrate           develop   gdp
## 1      1.8 Developed country 18054
## 2      1.8 Developed country 14000
## 3       NA Developed country   800
summary(Ca)
##              country      pop92            urban          lifeexpm   
##  Australia       :1   Min.   : 3.347   Min.   :15.00   Min.   :55.0  
##  New Zealand     :1   1st Qu.: 3.676   1st Qu.:45.50   1st Qu.:63.5  
##  Papua New Guinea:1   Median : 4.006   Median :76.00   Median :72.0  
##  Afghanistan     :0   Mean   : 8.307   Mean   :58.67   Mean   :67.0  
##  Albania         :0   3rd Qu.:10.787   3rd Qu.:80.50   3rd Qu.:73.0  
##  Algeria         :0   Max.   :17.567   Max.   :85.00   Max.   :74.0  
##  (Other)         :0                                                  
##     lifeexpf     birthrat        deathrat        infmr          fertrate  
##  Min.   :56   Min.   :15.00   Min.   : 8.0   Min.   : 8.00   Min.   :1.8  
##  1st Qu.:68   1st Qu.:15.00   1st Qu.: 8.0   1st Qu.: 9.00   1st Qu.:1.8  
##  Median :80   Median :15.00   Median : 8.0   Median :10.00   Median :1.8  
##  Mean   :72   Mean   :21.33   Mean   : 9.0   Mean   :24.33   Mean   :1.8  
##  3rd Qu.:80   3rd Qu.:24.50   3rd Qu.: 9.5   3rd Qu.:32.50   3rd Qu.:1.8  
##  Max.   :80   Max.   :34.00   Max.   :11.0   Max.   :55.00   Max.   :1.8  
##                                                              NA's   :1    
##                develop       gdp       
##  Developing country:0   Min.   :  800  
##  Developed country :3   1st Qu.: 7400  
##                         Median :14000  
##                         Mean   :10951  
##                         3rd Qu.:16027  
##                         Max.   :18054  
## 

Prepared By, Md. Mahdi Hasan, B,Sc. (Hon"s), Depatment of Statistics, Dhaka College, Dhaka.